Search Results for author: Faisal Mahmood

Found 31 papers, 16 papers with code

Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

no code implementations31 Oct 2022 Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot, Faisal Mahmood

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations.

Data Augmentation Multiple Instance Learning +1

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

1 code implementation17 Jun 2022 Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment.

Survival Prediction whole slide images

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

1 code implementation CVPR 2022 Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood

Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).

Self-Supervised Learning Survival Prediction

Algorithm Fairness in AI for Medicine and Healthcare

no code implementations1 Oct 2021 Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F. K. Williamson, Ming Y. Lu, Sharifa Sahai, Faisal Mahmood

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care.

Disentanglement Fairness +1

Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

1 code implementation4 Aug 2021 Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Jana Lipkova, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, Zahra Noor, Faisal Mahmood

To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.

Multimodal Deep Learning whole slide images

Fast and Scalable Image Search For Histology

1 code implementation28 Jul 2021 Chengkuan Chen, Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Andrew J. Schaumberg, Faisal Mahmood

Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes, and potential clinical trial success.

Image Retrieval Retrieval +1

Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

1 code implementation27 Jul 2021 Richard J. Chen, Ming Y. Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival.

Survival Prediction whole slide images

Deep Learning-based Frozen Section to FFPE Translation

1 code implementation25 Jul 2021 Kutsev Bengisu Ozyoruk, Sermet Can, Guliz Irem Gokceler, Kayhan Basak, Derya Demir, Gurdeniz Serin, Uguray Payam Hacisalihoglu, Emirhan Kurtuluş, Berkan Darbaz, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, Funda Yilmaz, Faisal Mahmood, Mehmet Turan

In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes.

Decision Making Translation +1

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

1 code implementation ICCV 2021 Richard J. Chen, Ming Y. Lu, Wei-Hung Weng, Tiffany Y. Chen, Drew F.K. Williamson, Trevor Manz, Maha Shady, Faisal Mahmood

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs).

Multiple Instance Learning Question Answering +5

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

1 code implementation21 Sep 2020 Ming Y. Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.

Federated Learning Multiple Instance Learning +3

VR-Caps: A Virtual Environment for Capsule Endoscopy

1 code implementation29 Aug 2020 Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid, Guliz Irem Gokceler, Kutsev Bengisu Ozyoruk, Yasin Almalioglu, Richard J. Chen, Faisal Mahmood, Hunter Gilbert, Nicholas J. Durr, Mehmet Turan

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions.

Depth Estimation Visual Localization

Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary

1 code implementation24 Jun 2020 Ming Y. Lu, Melissa Zhao, Maha Shady, Jana Lipkova, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood

Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.

whole slide images

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

1 code implementation20 Apr 2020 Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, Faisal Mahmood

CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.

Domain Adaptation Multiple Instance Learning +2

EndoL2H: Deep Super-Resolution for Capsule Endoscopy

2 code implementations13 Feb 2020 Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce, Kagan Incetan, Guliz Irem Gokceler, Muhammed Ali Simsek, Kivanc Ararat, Richard J. Chen, Nicholas J. Durr, Faisal Mahmood, Mehmet Turan

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics.

Super-Resolution

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

1 code implementation18 Dec 2019 Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal I. Lindeman, Faisal Mahmood

Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data.

Feature Importance

Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks

no code implementations29 Oct 2019 Jingwen Wang, Richard J. Chen, Ming Y. Lu, Alexander Baras, Faisal Mahmood

In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands.

Classification General Classification

SLAM Endoscopy enhanced by adversarial depth prediction

no code implementations29 Jun 2019 Richard J. Chen, Taylor L. Bobrow, Thomas Athey, Faisal Mahmood, Nicholas J. Durr

Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing.

Depth Prediction Monocular Depth Estimation +1

GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images

no code implementations12 Jun 2019 Mason T. Chen, Faisal Mahmood, Jordan A. Sweer, Nicholas J. Durr

In human gastrointestinal specimens, GANPOP estimates both reduced scattering and absorption coefficients at 660 nm from a single 0. 2/mm spatial frequency illumination image with 58% higher accuracy than SSOP.

Structured Prediction using cGANs with Fusion Discriminator

no code implementations ICLR 2019 Faisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan Yuille

We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation.

Depth Estimation Image Generation +2

Multimodal Densenet

no code implementations18 Nov 2018 Faisal Mahmood, Ziyun Yang, Thomas Ashley, Nicholas J. Durr

In this work, we propose Multimodal DenseNet, a novel architecture for fusing multimodal data.

DeepLSR: a deep learning approach for laser speckle reduction

2 code implementations23 Oct 2018 Taylor L. Bobrow, Faisal Mahmood, Miguel Inserni, Nicholas J. Durr

In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6. 4 dB, compared to a 2. 9 dB reduction from optimized non-local means processing, a 3. 0 dB reduction from BM3D, and a 3. 7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser.

Rethinking Monocular Depth Estimation with Adversarial Training

no code implementations22 Aug 2018 Richard Chen, Faisal Mahmood, Alan Yuille, Nicholas J. Durr

Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function.

Monocular Depth Estimation

Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images

no code implementations22 May 2018 Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J. Durr

Our experiments demonstrate that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model.

Monocular Depth Estimation

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training

no code implementations17 Nov 2017 Faisal Mahmood, Richard Chen, Nicholas J. Durr

We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization.

Depth Estimation Domain Adaptation

Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy

no code implementations30 Oct 2017 Faisal Mahmood, Nicholas J. Durr

We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images.

Depth Estimation

Adaptive Graph-based Total Variation for Tomographic Reconstructions

no code implementations4 Oct 2016 Faisal Mahmood, Nauman Shahid, Ulf Skoglund, Pierre Vandergheynst

Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions.

Image Reconstruction Tomographic Reconstructions

Graph Based Sinogram Denoising for Tomographic Reconstructions

no code implementations14 Mar 2016 Faisal Mahmood, Nauman Shahid, Pierre Vandergheynst, Ulf Skoglund

This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure.

Denoising Tomographic Reconstructions

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